Update math.py

math.py

@@ -0,0 +1,59 @@
+To calculate the value of x using NumPy, you can use the following approach:
+
+1. Import NumPy:
+
+```python
+import numpy as np
+```
+
+2. Set up the equation:
+
+```python
+x = 0.123456789**987654321.0
+```
+
+3. Use NumPy's `exp` and `log` functions to calculate this more accurately:
+
+```python
+ln_x = 987654321.0 * np.log(0.123456789)
+x = np.exp(ln_x)
+```
+
+4. Print the result:
+
+```python
+print(f"x = {x}")
+```
+
+The full code would look like this:
+
+```python
+import numpy as np
+
+ln_x = 987654321.0 * np.log(0.123456789)
+x = np.exp(ln_x)
+
+print(f"x = {x}")
+```
+
+This will output a value extremely close to zero. In fact, it's so small that it will likely be represented as 0.0 due to floating-point limitations.
+
+To get a sense of how small this number is, you can use scientific notation:
+
+```python
+print(f"x in scientific notation: {x:e}")
+```
+
+This will give you an idea of the order of magnitude of the result.
+
+Keep in mind that this number is so incredibly small that it's effectively zero for most practical purposes. It's smaller than the smallest number that can be represented in standard double-precision floating-point format.
+
+Sources
+[1] numpy's interp function - how to find a value of x for a given value of y? https://stackoverflow.com/questions/26439107/numpys-interp-function-how-to-find-a-value-of-x-for-a-given-value-of-y
+[2] numpy.exp — NumPy v2.0 Manual https://numpy.org/doc/stable/reference/generated/numpy.exp.html
+[3] numpy.absolute — NumPy v2.0 Manual https://numpy.org/doc/stable/reference/generated/numpy.absolute.html
+[4] Computation on NumPy Arrays: Universal Functions https://jakevdp.github.io/PythonDataScienceHandbook/02.03-computation-on-arrays-ufuncs.html
+[5] Chapter 3 Numerical calculations with NumPy http://kestrel.nmt.edu/~raymond/software/python_notes/paper003.html
+[6] numpy for Linear Algebra https://www2.lawrence.edu/fast/GREGGJ/Python/numpy/numpyLA.html
+[7] numpy.polyval — NumPy v2.0 Manual https://numpy.org/doc/stable/reference/generated/numpy.polyval.html
+[8] numpy.linalg.solve — NumPy v2.0 Manual https://numpy.org/doc/stable/reference/generated/numpy.linalg.solve.html